From df4cf57c7394265b27d051cb1cf0152c53659126 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Wed, 9 Oct 2019 15:32:39 +0100 Subject: COMPMID-2306: CLDepthwiseConvolution: support for QUANT8_PER_CHANNEL_SYMM Change-Id: I18c886400daa2dcba0b91011bc4e503d807a4732 Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/2143 Comments-Addressed: Arm Jenkins Reviewed-by: Giorgio Arena Tested-by: Arm Jenkins --- .../CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp | 134 +++++++++++++-------- 1 file changed, 85 insertions(+), 49 deletions(-) (limited to 'src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp') diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp index 42e5fbc8f2..a2f4a913ce 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp @@ -37,13 +37,15 @@ #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" -using namespace arm_compute; +namespace arm_compute +{ using namespace arm_compute::misc::shape_calculator; namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const ActivationLayerInfo &act_info, const Size2D dilation) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D dilation, + const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); @@ -52,7 +54,6 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC), "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3); ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); @@ -74,28 +75,43 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } - if(output->total_size() != 0) + if(is_qasymm) { - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); - } + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); - if(is_qasymm) + if(is_data_type_quantized_per_channel(weights->data_type())) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_multipliers->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_shifts->dimension(0)); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_RETURN_ERROR_ON(1 != output_multipliers->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(1 != output_shifts->dimension(0)); + } + } + else { - const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info; + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + } - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; - ARM_COMPUTE_UNUSED(multiplier); - ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f); + if(output->total_size() != 0) + { + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); } return Status{}; } -std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - GPUTarget gpu_target, std::string &kernel_name, const Size2D dilation) +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, GPUTarget gpu_target, std::string &kernel_name, const Size2D dilation) { // Output auto inizialitation if not yet initialized const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); @@ -182,9 +198,9 @@ std::pair validate_and_configure_window(ITensorInfo *input, ITen } else { - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_data_type_quantized_per_channel(weights->data_type()); - kernel_name = is_qasymm ? "dwc_3x3_native_qasymm8" : "depthwise_convolution_3x3"; + kernel_name = is_qasymm ? "dwc_3x3_native_quantized8" : "depthwise_convolution_3x3"; kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : ""); kernel_name += (is_qasymm ? "_nchw" : ""); @@ -224,23 +240,28 @@ BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const return _border_size; } -void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) +void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, + const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info, dilation)); - - bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); - - _input = input; - _output = output; - _weights = weights; - _biases = biases; - _conv_stride_x = conv_info.stride().first; - _conv_stride_y = conv_info.stride().second; - _conv_pad_left = conv_info.pad_left(); - _conv_pad_top = conv_info.pad_top(); - _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), + conv_info, depth_multiplier, act_info, dilation, + (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, + (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + + _input = input; + _output = output; + _weights = weights; + _biases = biases; + _conv_stride_x = conv_info.stride().first; + _conv_stride_y = conv_info.stride().second; + _conv_pad_left = conv_info.pad_left(); + _conv_pad_top = conv_info.pad_top(); + _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left); + _output_multipliers = output_multipliers; + _output_shifts = output_shifts; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); // Configure kernel window std::string kernel_name; @@ -260,24 +281,21 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); - if(is_qasymm) + if(_is_quantized) { const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform(); const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform(); const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform(); - float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; - int output_multiplier = 0; - int output_shift = 0; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - + const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type()); + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel; build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_info.offset)); - build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); - build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); + build_opts.add_option_if(is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); + build_opts.add_option_if(is_dot8_supported, "-DIS_DOT8"); if(act_info.enabled()) { @@ -293,6 +311,10 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); } + + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); + build_opts.add_option("-DWEIGHTS_PROMOTED_TYPE=" + get_cl_promoted_type_from_data_type(weights->info()->data_type())); } else { @@ -323,12 +345,15 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, _config_id += support::cpp11::to_string(output->info()->dimension(1)); } -Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation) +Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, + const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { std::string kernel_name; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, gpu_target, kernel_name, dilation).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), + conv_info, depth_multiplier, gpu_target, kernel_name, dilation) + .first); return Status{}; } @@ -353,18 +378,28 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::Com slice_weights.set_dimension_step(Window::DimX, 0); slice_weights.set_dimension_step(Window::DimY, 0); + unsigned int idx = 3 * num_arguments_per_3D_tensor(); + + // Set output multipliers in case of quantized data type + if(_is_quantized) + { + Window slice; + slice.use_tensor_dimensions(_output_multipliers->info()->tensor_shape()); + add_1D_tensor_argument(idx, _output_multipliers, slice); + add_1D_tensor_argument(idx, _output_shifts, slice); + } + // Set biases if(_biases != nullptr) { - unsigned int idx = 3 * num_arguments_per_3D_tensor(); - Window slice_biases; + Window slice_biases; slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); add_1D_tensor_argument(idx, _biases, slice_biases); } do { - unsigned int idx = 0; + idx = 0; add_3D_tensor_argument(idx, _input, slice_in); add_3D_tensor_argument(idx, _output, slice_out); add_3D_tensor_argument(idx, _weights, slice_weights); @@ -373,3 +408,4 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::Com } while(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in)); } +} // namespace arm_compute -- cgit v1.2.1